Hung-ting Wen
Technical Lead, Search Content Platform
Los Altos, California, United States
Summary
🤩
Rockstar🎓
Top SchoolHung-ting Wen is a Technical Lead based in Los Altos with 11 years of experience building exabyte-scale data backbones and AI infrastructure at Google, currently driving data acquisition and ingestion efficiency for Search's commercial verticals. He has led cross-functional teams to scale partner onboarding and index large partner feeds for products like Order with Google, and earlier built metrics and speech translation features for Google Translate. His background spans cloud-native reliability, service mesh and Kubernetes rollout stability, and hands-on MLOps work—he actively contributed deployment and Istio/IAP configuration fixes to the prominent Kubeflow open-source project. Comfortable moving between low-level infrastructure and product-facing systems, Hung-ting combines an MS in Computer Science from NYU with practical experience in NLP, real-time systems, and production ML deployments. Notably, his career began with a mix of operations and business roles, giving him an uncommon blend of engineering depth and operational pragmatism.
11 years of coding experience
9 years of employment as a software developer
Bachelor's degree International Business, Bachelor's degree International Business at National Chengchi UniversityMaster of Science (M.S.) Computer Science, Master of Science (M.S.) Computer Science at New York UniversityNon-Degree Program Computer Science, Non-Degree Program Computer Science at National Tsing Hua University
English, Chinese
Github Skills (14)
istio10
kustomize10
kubernetes10
cicd10
kubernetes-pods10
yaml10
gcp10
go9
mlops8
automations8
automation8
helm8
dockers7
docker7
Programming languages (9)
Github contributions (5)
kubeflow/kubeflow
Jan 2019 - Dec 2019
Machine Learning Toolkit for Kubernetes
Role in this project:
DevOps & MLOps Engineer
DevOps & MLOps EngineerContributions:6 releases, 113 commits, 109 PRs in 11 months
Contributions summary:Hung-ting primarily focused on improving the deployment and configuration of Kubeflow on Google Cloud Platform. Their commits reveal work on setting up and managing IAP authentication, implementing Istio routing rules for various Kubeflow components like Katib, Jupyter, and pipelines, and creating basic authentication setups. Further contributions include the creation and deletion of endpoint services and enhancements to the build process for kfctl and added features to facilitate the management of GCP resources.
pythondata-sciencenotebookmachine-learningmlops
kubeflow/manifests
Aug 2019 - Dec 2019
A repository for Kubeflow manifests
Role in this project:DevOps Engineer
DevOps EngineerContributions:1 release, 8 commits, 28 PRs in 3 months
Contributions summary:Hung-ting primarily focused on modifying and maintaining the Kubeflow deployment manifests. Their contributions involved fixing issues related to various components, including the tf-job-operator, pipeline UI, and IAP ingress. They addressed configuration problems related to secret names and storage, and they made multiple changes to adjust naming conventions within the pipeline. These modifications demonstrate a focus on ensuring the correct deployment and configuration of Kubeflow components.
kubeflowk8skuberneteskustomizemanifests
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